Apparatus and method for analyzing characteristic of game player in real time

ABSTRACT

Provided are an apparatus and method for analyzing a characteristic of a game player in real time. The apparatus includes at least one individual action determiner for calculating a player characteristic value indicating a level or tendency of a game player by giving a first weight to game play data values resulting from game play of the game player, an accuracy determiner for comparing the player characteristic value and a value set by an administrator, and calculating an accuracy value of the individual action determiner, a comprehensive level determiner for calculating a final characteristic value indicating a level or tendency of the game player by giving a second weight to the player characteristic values calculated by the individual action determiners when the accuracy value do not exceed a reference value, and a characteristic value output unit for outputting the final characteristic value.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of Korean PatentApplication No. 10-2009-0127997, filed Dec. 21, 2009, and Korean PatentApplication No. 10-2010-0057842, filed Jun. 18, 2010, the disclosure ofwhich is incorporated herein by reference in its entirety.

BACKGROUND

1. Field of the Invention

The present invention relates to an apparatus and method for analyzing acharacteristic of a game player in real time, and more particularly, toa real-time game player characteristic analysis apparatus and methodcapable of accurately determining a level and tendency of a game playerin real time.

2. Discussion of Related Art

In computer games, it is important for a game service to find a leveland tendency of a game player. Conventionally, a game player personallyinputs his/her level and tendency, or a level and tendency aredetermined according to a particular rule.

Such a level and tendency of a game player can express a person good atthe corresponding computer game and a person poor at the computer gameby scalar values such as a beginning level, an intermediate level and anadvanced level. According to the level and tendency of a game player, aprovided game environment may vary.

However, even if the same identification (ID) is used to play a computergame, a player may vary, and the player's proficiency may also vary.Furthermore, the level of a player may vary every moment. For example,the level of a player may become much higher than that originallydetected if the playing style of the game player matches a current gamesituation, or may be lowered if the playing style of the game playerdoes not match the current game situation. According to such asituational change, it is necessary to find the level and tendency of agame player in real time.

SUMMARY OF THE INVENTION

The present invention is directed to accurately finding a level andtendency of a game player in real time by hierarchically learning levelsand tendencies of the game player.

The present invention is also directed to increasing the degree ofadaptation of a game player to a game and keeping the game playerstrained and immersed in the game by adjusting the difficulty of thegame according to the level of the game player.

The present invention is also directed to providing a game playermatching service and an appropriate guide or training scenario accordingto the level of a game player.

One aspect of the present invention provides an apparatus for analyzinga characteristic of a game player in real time including: at least oneindividual action determiner for calculating a player characteristicvalue indicating a level or tendency of a game player by giving a firstweight to game play data values resulting from game play of the gameplayer; an accuracy determiner for comparing the player characteristicvalue and a value set by an administrator, and calculating an accuracyvalue of the individual action determiner; a comprehensive leveldeterminer for calculating a final characteristic value indicating alevel or tendency of the game player by giving a second weight to theplayer characteristic values calculated by the individual actiondeterminers when the accuracy value does not exceed a reference value;and a characteristic value output unit for outputting the finalcharacteristic value.

Another aspect of the present invention provides a method of analyzing acharacteristic of a game player in real time including: calculating aplayer characteristic value indicating a level or tendency of a gameplayer by giving a first weight to game play data values resulting fromgame play of the game player; comparing the player characteristic valueand a value set by an administrator, and calculating an accuracy value;calculating a final characteristic value indicating a level or tendencyof the game player by giving a second weight to the playercharacteristic values when the accuracy value does not exceed areference value; and outputting the final characteristic value.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentinvention will become more apparent to those of ordinary skill in theart by describing in detail exemplary embodiments thereof with referenceto the attached drawings, in which:

FIGS. 1 and 2 illustrate an apparatus for analyzing a characteristic ofa game player in real time according to an exemplary embodiment of thepresent invention;

FIG. 3 is a block diagram of an apparatus for analyzing a characteristicof a game player in real time according to an exemplary embodiment ofthe present invention;

FIGS. 4 and 5 illustrate a real-time game player characteristic analysisapparatus constituted of a neural network according to an exemplaryembodiment of the present invention; and

FIG. 6 is a flowchart illustrating a method of analyzing acharacteristic of a game player in real time according to an exemplaryembodiment of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, exemplary embodiments of the present invention will bedescribed in detail. However, the present invention is not limited tothe embodiments disclosed below but can be implemented in various forms.The following embodiments are described in order to enable those ofordinary skill in the art to embody and practice the present invention.To clearly describe the present invention, parts not relating to thedescription are omitted from the drawings. Like numerals refer to likeelements throughout the description of the drawings.

FIGS. 1 and 2 illustrate an apparatus for analyzing a characteristic ofa game player in real time according to an exemplary embodiment of thepresent invention.

An apparatus 110 for analyzing a characteristic of a game player in realtime according to an exemplary embodiment of the present inventiondetermines whether the accuracy of a result value calculated by giving aweight to an input simulation data value exceeds a previously set value.At this time, the apparatus 110 compares the result value and asimulation result value to calculate the accuracy. The more similar theresult value and the simulation result value, the higher the accuracy.

When it is determined that the accuracy of the result value exceeds thepreviously set value, the apparatus 110 calculates a characteristicvalue using the weight.

As illustrated in FIG. 1, the apparatus 110 gives the weight to gameplay data values 111, 112, 113, 114 and 115 resulting from game play ofa game player, thereby calculating a player characteristic valueindicating a level or tendency of the game player.

Game play data values according to an exemplary embodiment of thepresent invention denote game environment values of a game player, suchas game environment variables 111, 112 and 113, a selected action value114 of the game player, a response time value 115 of the game player,and so on. Characteristic values of a game player denote a level value121 indicating the level of the game player, a tendency value 122indicating the tendency of the game player, and so on.

For example, time that passes after a character of the game player hasencountered an enemy, the number of times that the character shoots theenemy, the accuracy of the shots, whether or not the character iscovered by a cover, etc. is input to the apparatus 110 as a game playdata value, and a level value, a tendency value, etc. indicating how thegame player plays the game can be output. In other words, the apparatus110 for analyzing a characteristic of a game player in real timeaccording to an exemplary embodiment of the present invention can find acharacteristic, such as a level or tendency, of a game player accordingto a behavior pattern dependent on the situation of the game player.Here, the apparatus 110 can analyze a characteristic value of a gameplayer on the basis of one of a neural network, a multilayer perceptron(MLP), a genetic algorithm, a hidden Markov model (HMM), a Markov randomfield (MRF), a distributed system object model (DSOM), a fuzzy method,an evolution method, and a reinforcement method.

When the apparatus 110 analyzes the level or tendency of a game playerand the corresponding game is simple, the apparatus 110 can efficientlyanalyze the level or tendency. On the other hand, when the game is notsimple, a dimension increases with increases in the number of input playdata values and the number of output characteristic values of the gameplayer. Thus, analysis performance rapidly deteriorates, and it isdifficult to find the level or tendency of the game player in real time.Also, an unnecessary play data value may be taken into consideration,and an incorrect output value may be obtained.

For these reasons, a process of calculating a player characteristicvalue is divided in an exemplary embodiment of the present invention.For example, when the number of game play data input values input to theapparatus 110 is 20 and the number of output values is three,hidden=input*1.5=20*1.5=30, 20*hidden+hidden*3=20*30+30*3=690, and thus690 links are generated. On the other hand, when 20 inputs are dividedinto two groups of ten inputs each and the respective groups are inputto first and second modules, (10*15+15*3)*2=390, and thus 390 links aregenerated. Also, when the number of outputs of each of the first andsecond modules is three and all the six outputs of the first and secondmodules are input to a third module, 6*9+9*3=81, and thus 81 links aregenerated. As a result, 390+81=471, and a total of 471 links aregenerated. As described above, when a module is divided into smallermodules, the number of links is reduced, and performance can beimproved.

Thus, in an exemplary embodiment of the present invention, the apparatus110 divides the process of calculating a characteristic value of a gameplayer into two processes as illustrated in FIGS. 1 and 2, therebyanalyzing a characteristic, such as a level or tendency, of a gameplayer in real time. In other words, the apparatus 110 calculates playercharacteristic values 121 or 122 indicating the level or tendency of agame player as illustrated in FIG. 1, and gives a weight to calculatedplayer characteristic values 211, 212, 213 and 214 to determine a finallevel 221 or a final tendency 222 of the game player as illustrated inFIG. 2.

Also, in an exemplary embodiment of the present invention, the apparatus110 may compare the calculated player characteristic value with a valuepreviously set by an administrator to calculate an accuracy value, andmay calculate a final characteristic value using a plurality of playercharacteristic values only when the calculated accuracy value does notexceed the reference value.

To be specific, the apparatus 110 calculates a player characteristicvalue, compares the calculated player characteristic value with a valuepreviously set by an administrator to calculate an accuracy value, andoutputs a characteristic value such as the level 121 or the tendency 122of a game player as shown in FIG. 1 when the calculated accuracy valueexceeds the reference value. On the other hand, when the calculatedaccuracy value does not exceed the reference value, each of a pluralityof real-time game player characteristic analysis apparatuses shown inFIG. 1 receives input game play data and performs a playercharacteristic value calculation process, and as illustrated in FIG. 2,a real-time game player characteristic analysis apparatus gives a weightto the calculated player characteristic values 211, 212 and 213, thevariable 214, etc., thereby calculating a final characteristic value 221or 222 indicating the level or tendency of the game player. For example,when the calculated accuracy value does not exceed 90%, a finalcharacteristic value calculation process as illustrated in FIG. 2 isadditionally performed.

It will be described in detail below how the apparatus 110 calculates anaccuracy value.

As shown in Equation 1 below, the apparatus 110 calculates an accuracyvalue using a value set by an administrator and a player characteristicvalue calculated by the apparatus 110 itself.Accuracy=(Maximum−|Value Set by Administrator−Player CharacteristicValue|)/(Maximum−Minimum)*100(%)  [Equation 1]

As shown in Equation 1, the smaller a difference between the value setby an administrator and the player characteristic value of the apparatus110, the higher the accuracy value. For example, when a leveldetermination value of a specific game player ranges from 0 to 1, thevalue set by an administrator is 0.9, and the player characteristicvalue calculated by the apparatus 110 is 0.8,(1−|0.9−0.8|)/(1.0−0.0)*100(%)=90%, that is, accuracy is calculated tobe 90%.

When the accuracy value is sufficiently high as calculated above, theapparatus 110 finishes operation after calculating a characteristicvalue indicating the level 121 or the tendency 122 of a game player asillustrated in FIG. 1. On the other hand, when the calculated accuracyvalue is low, the apparatus 110 calculates a plurality of playercharacteristic values, and inputs the calculated player characteristicvalues to calculate final characteristic values, that is, the finallevel 221 and the final tendency 222 of the game player. For example,when the accuracy of the calculated characteristic value is 90% orabove, the calculated characteristic value has high reliability. Thus,after calculating the player characteristic value once, the apparatus110 outputs the calculated player characteristic value and finishesoperation, and the process of calculating a final characteristic valueusing a plurality of player characteristic values does not need to beperformed.

To improve such accuracy, an input simulation data value may be changed(e.g., an absolute position value of a game player's character input bythe game player is changed to a relative position value of thecharacter), or the simulation data value may be changed to anappropriate value by normalizing values such as response times, selectedactions, or game environments, or preprocessing the values into anaverage value.

FIG. 3 is a block diagram of an apparatus for analyzing a characteristicof a game player in real time according to an exemplary embodiment ofthe present invention. A constitution of the apparatus for analyzing acharacteristic of a game player in real time according to an exemplaryembodiment of the present invention will be described with reference toFIG. 3.

The apparatus 110 for analyzing a characteristic of a game player inreal time according to an exemplary embodiment of the present inventionincludes a learning processor 310, an accuracy determiner 315, aplurality of individual action determiners 320, 321 and 322, acomprehensive level determiner 330, and a characteristic value outputunit 340.

The learning processor 310 inputs a simulation data value to theindividual action determiners 320, 321 and 322 to give a random weightto the simulation data value and calculate a result value, and theaccuracy determiner 315 compares the calculated result value with asimulation result value to calculate an accuracy value. The learningprocessor 310 sets the individual action determiners 320, 321 and 322 toselect and use the random weight when the calculated accuracy valueexceeds a previously set value. Likewise, the learning processor 310causes the comprehensive level determiner 330 to calculate a resultvalue using a random weight, and the accuracy determiner 315 to comparethe calculated result value with a simulation result value and calculatean accuracy value. The learning processor 310 sets the comprehensivelevel determiner 330 to select and use the random weight when thecalculated accuracy value exceeds a previously set value.

The individual action determiners 320, 321 and 322 give a weight, whichis set as described above, to game play data values resulting from gameplay of a game player, thereby calculating a player character valueindicating the level or tendency of the game player.

The accuracy determiner 315 compares the player characteristic valuewith a value set by an administrator and calculates an accuracy value ofthe individual action determiners 320, 321 and 322.

When the calculated accuracy value does not exceed a reference value,the comprehensive level determiner 330 performs a calculation processagain using the player characteristic values calculated by theindividual action determiners 320, 321 and 322. In other words, thecomprehensive level determiner 330 gives a weight to the characteristicvalues calculated by the individual action determiners 320, 321 and 322to calculate a final characteristic value of the game player. Here, thefinal characteristic value denotes the level or tendency of the gameplayer.

The characteristic value output unit 340 outputs the playercharacteristic value calculated by the individual action determiners320, 321 and 322, or the final characteristic value calculated by thecomprehensive level determiner 330.

Here, the play data value denotes a variety of data, such as a responsetime value, a selected action value, or a game environment value of thegame player, generated when the game player plays the game, and theplayer characteristic value and the final characteristic value of thegame player are level values indicating the level of the game player ortendency values indicating the tendency of the game player.

Meanwhile, when it is determined that the accuracy value calculated bythe accuracy determiner 315 exceeds the reference value, thecharacteristic value output unit 340 outputs the player characteristicvalue calculated by the individual action determiners 320, 321 and 322.

For example, the learning processor 310 sets a weight, and the firstindividual action determiner 320 gives the weight to game play datavalues of the game player to calculate a player characteristic value.The comprehensive level determiner 330 compares the playercharacteristic value with the value set by the administrator andcalculates an accuracy value of the first individual action determiner320. When the accuracy determiner 315 determines that the accuracy valueof the first individual action determiner 320 exceeds the referencevalue, a player characteristic value calculated by inputting a game playdata value to the first individual action determiner 320 is outputthrough the characteristic value output unit 340.

On the other hand, when the accuracy determiner 315 determines that theaccuracy value of the first individual action determiner 320 does notexceed the reference value, the comprehensive level determiner 330calculates a final characteristic value of the game player using theplayer characteristic values calculated by the individual actiondeterminers 320, 321 and 322, and the characteristic value output unit340 outputs the calculated final characteristic value.

In an exemplary embodiment of the present invention, the individualaction determiners 320, 321 and 322 and the comprehensive leveldeterminer 330 can calculate a characteristic value of the game playeron the basis of one of a neural network, an MLP, a genetic algorithm, anHMM, an MRF, a DSOM, a fuzzy method, an evolution method, and areinforcement method.

FIGS. 4 and 5 illustrate a real-time game player characteristic analysisapparatus constituted of a neural network according to an exemplaryembodiment of the present invention.

The apparatus 110 constituted of a neural network according to anexemplary embodiment of the present invention will be described belowwith reference to FIGS. 4 and 5, but is not limited to the constitutionbased on a neural network.

As shown in FIG. 4, the apparatus 110 according to an exemplaryembodiment of the present invention is constituted of a neural network,and gives a weight to game environment values, such as game environmentvariables 411, 412 and 413, and player characteristic values, such as aselected action value 414 and a response time value 415 of a gameplayer, to calculate a level 421 and a tendency 422 of the game player.The weight is selected when an accuracy value calculated after theapparatus 110 receives a simulation data value, calculates a resultvalue, and compares the result value and a simulation result value, ishigh.

Also, the apparatus 110 according to an exemplary embodiment of thepresent invention inputs the level value 421 and the tendency value 422of the game player calculated by the neural network shown in FIG. 4 aslevel or tendency values 511, 512 and 513 as shown in FIG. 5, inputs anenvironment variable value 514, and gives a weight to the input values,thereby calculating a final level 521 and a final tendency 522 of thegame player.

Furthermore, a position value of a character of the game player or anobject is input to the apparatus 110 in a method varying according towhether the position value of the character or object is a relativeposition value or an absolute position value, or whether the game is atwo-dimensional (2D) game or a three-dimensional (3D) game. Thus, whenan absolute position value is input in an exemplary embodiment of thepresent invention, a current position value is normalized for use with amovement range set from 0 to 1. On the other hand, when a relativeposition value is input, the current position value is normalized foruse with a difference or the maximum distance between a referenceposition value and the current position value set to 1.

The level of a game player calculated by the apparatus 110 constitutedof a neural network may be indicated by 0 to 1. The lower the value, thelower the level of the game player. The higher the value, the higher thelevel. Also, the tendency of the game player may be referred to as anoffensive type, defensive type, offensive and defensive type, etc. Whenthe tendency is indicated by 0 to 1, the offensive type, defensive type,offensive and defensive type, etc. may be indicated by 0, 1, 0.5, etc.,respectively.

The apparatus 110 constituted of a neural network receives game playinformation on a game player in real time, and outputs level or tendencyinformation on the game player according to the game play information.

FIG. 6 is a flowchart illustrating a method of analyzing acharacteristic of a game player in real time according to an exemplaryembodiment of the present invention.

As illustrated in FIG. 6, a real-time game player characteristicanalysis apparatus sets a weight (S610). To be specific, the real-timegame player characteristic analysis apparatus receives a simulation datavalue, gives a random weight to the simulation data value to calculate aresult value, and compares the calculated result value with a simulationresult value to calculate an accuracy value. When the calculatedaccuracy value exceeds a previously set value, the real-time game playercharacteristic analysis apparatus selects and uses the random weight asa weight.

After this, the real-time game player characteristic analysis apparatusgives the weight to game play data values resulting from game play of agame player, thereby calculating a player characteristic value (S615).

Also, the real-time game player characteristic analysis apparatuscompares the calculated player characteristic value and a value set byan administrator, thereby calculating an accuracy value (S620).

The real-time game player characteristic analysis apparatus determineswhether the accuracy value exceeds a reference value (S625). When thecalculated accuracy value exceeds the reference value, the real-timegame player characteristic analysis apparatus outputs the calculatedplayer characteristic value (S630). To be specific, since the accuracyvalue exceeds the reference value, the calculated player characteristicvalue is determined to be sufficiently reliable and thus is outputwithout calculating a final characteristic value using a plurality ofplayer characteristic values.

Here, the play data values may be at least one of response time values,selected action values, and game environment values of the game player,and each of the player characteristic value and the final characteristicvalue of the game player is at least one of a level value indicating thelevel of the game player and a tendency value indicating the tendency ofthe game player.

Meanwhile, when it is determined that the calculated accuracy value doesnot exceed the reference value, the real-time game player characteristicanalysis apparatus gives the weight to the plurality of playercharacteristic values to calculate a final characteristic value of thegame player (S635 and S640), and outputs the calculated finalcharacteristic value (S645).

In an exemplary embodiment of the present invention, the real-time gameplayer characteristic analysis apparatus can calculate a characteristicvalue of the game player on the basis of one of a neural network, anMLP, a genetic algorithm, an HMM, an MRF, a DSOM, a fuzzy method, anevolution method, and a reinforcement method.

In an exemplary embodiment, of the present invention, it is possible toaccurately determine a level and tendency of a game player in real timeby hierarchically learning levels and tendencies of the game player inreal time and.

Consequently, in an exemplary embodiment of the present invention, it ispossible to increase the degree of adaptation of a game player to a gameand keep the game player strained and immersed in the game by adjustingthe difficulty of the game according to a level of the game player.Also, it is possible to provide a game player matching service and anappropriate guide or training scenario according to the level of a gameplayer.

The above-described exemplary embodiments of the present invention maybe implemented in various ways. For example, the exemplary embodimentsmay be implemented using hardware, software, or a combination thereof.The exemplary embodiments may be coded as software executable on one ormore processors that employ any one of a variety of operating systems orplatforms. Additionally, such software may be written using any of anumber of suitable programming languages, and also may be compiled asexecutable machine language code or intermediate code that is executedon a framework or virtual machine.

Also, the present invention may be embodied as a computer readablemedium (e.g., a computer memory, one or more floppy discs, compactdiscs, optical discs, magnetic tapes, flash memories) storing one ormore programs that perform methods for implementing the variousembodiments of the present invention discussed above when executed onone or more computers or other processors.

Using an exemplary embodiment of the present invention, it is possibleto accurately determine a level and tendency of a game player in realtime by hierarchically learning levels and tendencies of the gameplayer.

Also, it is possible to increase the degree of adaptation of a gameplayer to a game and keep the game player strained and immersed in thegame by adjusting the difficulty of the game according to the level ofthe game player.

Furthermore, it is possible to provide a game player matching serviceand an appropriate guide or training scenario according to the level ofa game player.

While the invention has been shown and described with reference tocertain exemplary embodiments thereof, it will be understood by thoseskilled in the art that various changes in form and details may be madetherein without departing from the spirit and scope of the invention asdefined by the appended claims.

1. An apparatus for analyzing a characteristic of a game player in realtime, comprising: at least one individual action determiner forcalculating a player characteristic value indicating a level or tendencyof a game player by giving a first weight to game play data valuesresulting from game play of the game player; an accuracy determiner forcomparing the player characteristic value and a value set by anadministrator, and calculating an accuracy value of the individualaction determiner; a comprehensive level determiner for calculating afinal characteristic value indicating a level or tendency of the gameplayer by giving a second weight to the player characteristic valuescalculated by the individual action determiners when the accuracy valuedoes not exceed a reference value; and a characteristic value outputunit for outputting the final characteristic value.
 2. The apparatus ofclaim 1, wherein the characteristic value output unit outputs the playercharacteristic value calculated by the individual action determiner whenthe accuracy value calculated by the accuracy determiner exceeds thereference value.
 3. The apparatus of claim 1, further comprising alearning processor for inputting a simulation data value to theindividual action determiner, controlling the individual actiondeterminer to calculate a result value by giving the first weight to thesimulation data value, controlling the accuracy, determiner to comparethe result value and a simulation result value and calculate an accuracyvalue, and setting the individual action determiner to use the firstweight when the calculated accuracy value exceeds a previously setvalue.
 4. The apparatus of claim 3, wherein the learning processorinputs a simulation data value to the comprehensive level determiner,controls the comprehensive level determiner to calculate a result valueby giving the second weight to the simulation data value, controls theaccuracy determiner to compare the result value and a simulation resultvalue and calculate an accuracy value, and sets the individual actiondeterminer to use the second weight when the calculated accuracy valueexceeds a previously set value.
 5. The apparatus of claim 1, wherein theindividual action determiner calculates the player characteristic valueby normalizing the game play data values or preprocessing the game playdata values into an average value.
 6. The apparatus of claim 1, whereinthe play data values are at least one of response time values, selectedaction values, and game environment values of the game player, and eachof the player characteristic value and the final characteristic value ofthe game player is at least one of a level value indicating a level ofthe game player and a tendency value indicating a tendency of the gameplayer.
 7. The apparatus of claim 1, wherein the at least one of theindividual action determiner and the comprehensive level determinercalculates the player characteristic value or the final characteristicvalue on the basis of at least one of a neural network, a multilayerperceptron (MLP), a genetic algorithm, a hidden Markov model (HMM), aMarkov random field (MRF), a distributed system object model (DSOM), afuzzy method, an evolution method, and a reinforcement method.
 8. Amethod of analyzing a characteristic of a game player in real time,comprising: calculating a player characteristic value indicating a levelor tendency of a game player by giving a first weight to game play datavalues resulting from game play of the game player; comparing the playercharacteristic value and a value set by an administrator, andcalculating an accuracy value; calculating a final characteristic valueindicating a level or tendency of the game player by giving a secondweight to the player characteristic values when the accuracy value doesnot exceed a reference value; and outputting the final characteristicvalue.
 9. The method of claim 8, further comprising, before calculatinga player characteristic value, setting the first weight or the secondweight to be used when a result value calculated by giving the firstweight or the second weight to a simulation data value exceeds apreviously set value.